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Abstract #2617

High Efficient Reconstruction Method for IVIM Imaging Based on Deep Neural Network and Synthetic Training Data and its Application in  IVIM-DKI

Lu Wang1, Zhen Xing2, Jian Wu1, Qinqin Yang1, Congbo Cai1, Shuhui Cai 1, Zhong Chen1, and Dairong Cao2
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China

Intravoxel incoherent motion (IVIM) imaging is a non-invasive MR perfusion imaging that could prevent patients from the harm of exogenous reagent. Previous studies proved that the least square and Bayesian approaches are so far the best algorithms in IVIM fitting. However, they still suffer from time-consuming and high noise level. We proposed a deep neural network-based reconstruction method with synthetic training data for IVIM imaging and extended it to hybrid IVIM-DKI (diffusion kurtosis imaging) model fitting. Experimental results show that our method owns prominent performance in both image quality and accuracy of fitting results with a remarkably short reconstruction time.

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